"""
===================================
Brain segmentation with median_otsu
===================================

We show how to extract brain information and mask from a b0 image using DIPY_'s
``segment.mask`` module.

First import the necessary modules:
"""

import numpy as np
from dipy.data import get_fnames
from dipy.io.image import load_nifti, save_nifti
from dipy.segment.mask import median_otsu

###############################################################################
# Download and read the data for this tutorial.
# 
# The ``scil_b0`` dataset contains different data from different companies and
# models. For this example, the data comes from a 1.5 Tesla Siemens MRI.


data_fnames = get_fnames('scil_b0')
data, affine = load_nifti(data_fnames[1])
data = np.squeeze(data)

###############################################################################
# Segment the brain using DIPY's ``mask`` module.
# 
# ``median_otsu`` returns the segmented brain data and a binary mask of the
# brain. It is possible to fine tune the parameters of ``median_otsu``
# (``median_radius`` and ``num_pass``) if extraction yields incorrect results
# but the default parameters work well on most volumes. For this example,
# we used 2 as ``median_radius`` and 1 as ``num_pass``


b0_mask, mask = median_otsu(data, median_radius=2, numpass=1)

###############################################################################
# Saving the segmentation results is very easy. We need the ``b0_mask``, and the
# binary mask volumes. The affine matrix which transform the image's coordinates
# to the world coordinates is also needed. Here, we choose to save both images
# in ``float32``.


fname = 'se_1.5t'
save_nifti(fname + '_binary_mask.nii.gz', mask.astype(np.float32), affine)
save_nifti(fname + '_mask.nii.gz', b0_mask.astype(np.float32), affine)

###############################################################################
# Quick view of the results middle slice using ``matplotlib``.


import matplotlib.pyplot as plt
from dipy.core.histeq import histeq

sli = data.shape[2] // 2
plt.figure('Brain segmentation')
plt.subplot(1, 2, 1).set_axis_off()
plt.imshow(histeq(data[:, :, sli].astype('float')).T,
           cmap='gray', origin='lower')

plt.subplot(1, 2, 2).set_axis_off()
plt.imshow(histeq(b0_mask[:, :, sli].astype('float')).T,
           cmap='gray', origin='lower')

plt.savefig('median_otsu.png')

###############################################################################
# .. figure:: median_otsu.png
#    :align: center
# 
#    An application of median_otsu for brain segmentation.
# 
# ``median_otsu`` can also automatically crop the outputs to remove the largest
# possible number of background voxels. This makes outputted data significantly
# smaller. Auto-cropping in ``median_otsu`` is activated by setting the
# ``autocrop`` parameter to ``True``.


b0_mask_crop, mask_crop = median_otsu(data, median_radius=4, numpass=4,
                                      autocrop=True)

###############################################################################
# Saving cropped data using nibabel as demonstrated previously.
# 
# .. include:: ../links_names.inc


save_nifti(fname + '_binary_mask_crop.nii.gz', mask_crop.astype(np.float32),
           affine)
save_nifti(fname + '_mask_crop.nii.gz', b0_mask_crop.astype(np.float32),
           affine)